Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations1200000
Missing cells1203748
Missing cells (%)4.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory192.3 MiB
Average record size in memory168.0 B

Variable types

Numeric9
Categorical10
DateTime1
Boolean1

Alerts

Age has 18705 (1.6%) missing values Missing
Annual Income has 44949 (3.7%) missing values Missing
Marital Status has 18529 (1.5%) missing values Missing
Number of Dependents has 109672 (9.1%) missing values Missing
Occupation has 358075 (29.8%) missing values Missing
Health Score has 74076 (6.2%) missing values Missing
Previous Claims has 364029 (30.3%) missing values Missing
Credit Score has 137882 (11.5%) missing values Missing
Customer Feedback has 77824 (6.5%) missing values Missing
id is uniformly distributed Uniform
id has unique values Unique
Previous Claims has 305433 (25.5%) zeros Zeros
Vehicle Age has 61615 (5.1%) zeros Zeros

Reproduction

Analysis started2025-08-01 05:05:19.058691
Analysis finished2025-08-01 05:06:10.217182
Duration51.16 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

Uniform  Unique 

Distinct1200000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean599999.5
Minimum0
Maximum1199999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size9.2 MiB
2025-08-01T14:06:10.262979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile59999.95
Q1299999.75
median599999.5
Q3899999.25
95-th percentile1139999.1
Maximum1199999
Range1199999
Interquartile range (IQR)599999.5

Descriptive statistics

Standard deviation346410.31
Coefficient of variation (CV)0.57735099
Kurtosis-1.2
Mean599999.5
Median Absolute Deviation (MAD)300000
Skewness3.8362792 × 10-16
Sum7.199994 × 1011
Variance1.200001 × 1011
MonotonicityStrictly increasing
2025-08-01T14:06:10.407534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
799988 1
 
< 0.1%
800004 1
 
< 0.1%
800003 1
 
< 0.1%
800002 1
 
< 0.1%
800001 1
 
< 0.1%
800000 1
 
< 0.1%
799999 1
 
< 0.1%
799998 1
 
< 0.1%
799997 1
 
< 0.1%
Other values (1199990) 1199990
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
1199999 1
< 0.1%
1199998 1
< 0.1%
1199997 1
< 0.1%
1199996 1
< 0.1%
1199995 1
< 0.1%
1199994 1
< 0.1%
1199993 1
< 0.1%
1199992 1
< 0.1%
1199991 1
< 0.1%
1199990 1
< 0.1%

Age
Real number (ℝ)

Missing 

Distinct47
Distinct (%)< 0.1%
Missing18705
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean41.145563
Minimum18
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 MiB
2025-08-01T14:06:10.459556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q130
median41
Q353
95-th percentile62
Maximum64
Range46
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.53995
Coefficient of variation (CV)0.32907436
Kurtosis-1.194939
Mean41.145563
Median Absolute Deviation (MAD)12
Skewness-0.012531918
Sum48605048
Variance183.33024
MonotonicityNot monotonic
2025-08-01T14:06:10.507889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
53 26354
 
2.2%
61 26218
 
2.2%
39 26042
 
2.2%
64 25990
 
2.2%
57 25971
 
2.2%
43 25966
 
2.2%
62 25849
 
2.2%
46 25756
 
2.1%
33 25728
 
2.1%
47 25709
 
2.1%
Other values (37) 921712
76.8%
ValueCountFrequency (%)
18 24488
2.0%
19 24641
2.1%
20 25055
2.1%
21 24987
2.1%
22 25309
2.1%
23 23225
1.9%
24 24690
2.1%
25 24221
2.0%
26 24805
2.1%
27 24212
2.0%
ValueCountFrequency (%)
64 25990
2.2%
63 24283
2.0%
62 25849
2.2%
61 26218
2.2%
60 24593
2.0%
59 25173
2.1%
58 25544
2.1%
57 25971
2.2%
56 25450
2.1%
55 25132
2.1%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.2 MiB
Male
602571 
Female
597429 

Length

Max length6
Median length4
Mean length4.995715
Min length4

Characters and Unicode

Total characters5994858
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 602571
50.2%
Female 597429
49.8%

Length

2025-08-01T14:06:10.557320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-01T14:06:10.593437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male 602571
50.2%
female 597429
49.8%

Most occurring characters

ValueCountFrequency (%)
e 1797429
30.0%
a 1200000
20.0%
l 1200000
20.0%
M 602571
 
10.1%
F 597429
 
10.0%
m 597429
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5994858
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1797429
30.0%
a 1200000
20.0%
l 1200000
20.0%
M 602571
 
10.1%
F 597429
 
10.0%
m 597429
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5994858
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1797429
30.0%
a 1200000
20.0%
l 1200000
20.0%
M 602571
 
10.1%
F 597429
 
10.0%
m 597429
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5994858
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1797429
30.0%
a 1200000
20.0%
l 1200000
20.0%
M 602571
 
10.1%
F 597429
 
10.0%
m 597429
 
10.0%

Annual Income
Real number (ℝ)

Missing 

Distinct88593
Distinct (%)7.7%
Missing44949
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean32745.218
Minimum1
Maximum149997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 MiB
2025-08-01T14:06:10.631187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1206
Q18001
median23911
Q344634
95-th percentile104539
Maximum149997
Range149996
Interquartile range (IQR)36633

Descriptive statistics

Standard deviation32179.506
Coefficient of variation (CV)0.98272384
Kurtosis1.7949702
Mean32745.218
Median Absolute Deviation (MAD)17171
Skewness1.4703575
Sum3.7822397 × 1010
Variance1.0355206 × 109
MonotonicityNot monotonic
2025-08-01T14:06:10.683793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7073 1039
 
0.1%
16054 1019
 
0.1%
14094 916
 
0.1%
24897 914
 
0.1%
15983 888
 
0.1%
7991 877
 
0.1%
13982 872
 
0.1%
16076 852
 
0.1%
17091 762
 
0.1%
16891 759
 
0.1%
Other values (88583) 1146153
95.5%
(Missing) 44949
 
3.7%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 7
< 0.1%
3 4
< 0.1%
5 4
< 0.1%
7 1
 
< 0.1%
8 3
 
< 0.1%
10 1
 
< 0.1%
11 8
< 0.1%
13 2
 
< 0.1%
14 2
 
< 0.1%
ValueCountFrequency (%)
149997 2
 
< 0.1%
149996 17
< 0.1%
149995 5
 
< 0.1%
149994 1
 
< 0.1%
149993 5
 
< 0.1%
149992 7
< 0.1%
149991 3
 
< 0.1%
149990 6
 
< 0.1%
149989 3
 
< 0.1%
149987 4
 
< 0.1%

Marital Status
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing18529
Missing (%)1.5%
Memory size9.2 MiB
Single
395391 
Married
394316 
Divorced
391764 

Length

Max length8
Median length7
Mean length6.9969301
Min length6

Characters and Unicode

Total characters8266670
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarried
2nd rowDivorced
3rd rowDivorced
4th rowMarried
5th rowSingle

Common Values

ValueCountFrequency (%)
Single 395391
32.9%
Married 394316
32.9%
Divorced 391764
32.6%
(Missing) 18529
 
1.5%

Length

2025-08-01T14:06:10.735596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-01T14:06:10.764910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
single 395391
33.5%
married 394316
33.4%
divorced 391764
33.2%

Most occurring characters

ValueCountFrequency (%)
i 1181471
14.3%
e 1181471
14.3%
r 1180396
14.3%
d 786080
9.5%
S 395391
 
4.8%
n 395391
 
4.8%
g 395391
 
4.8%
l 395391
 
4.8%
M 394316
 
4.8%
a 394316
 
4.8%
Other values (4) 1567056
19.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8266670
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1181471
14.3%
e 1181471
14.3%
r 1180396
14.3%
d 786080
9.5%
S 395391
 
4.8%
n 395391
 
4.8%
g 395391
 
4.8%
l 395391
 
4.8%
M 394316
 
4.8%
a 394316
 
4.8%
Other values (4) 1567056
19.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8266670
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1181471
14.3%
e 1181471
14.3%
r 1180396
14.3%
d 786080
9.5%
S 395391
 
4.8%
n 395391
 
4.8%
g 395391
 
4.8%
l 395391
 
4.8%
M 394316
 
4.8%
a 394316
 
4.8%
Other values (4) 1567056
19.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8266670
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1181471
14.3%
e 1181471
14.3%
r 1180396
14.3%
d 786080
9.5%
S 395391
 
4.8%
n 395391
 
4.8%
g 395391
 
4.8%
l 395391
 
4.8%
M 394316
 
4.8%
a 394316
 
4.8%
Other values (4) 1567056
19.0%

Number of Dependents
Categorical

Missing 

Distinct5
Distinct (%)< 0.1%
Missing109672
Missing (%)9.1%
Memory size9.2 MiB
3.0
221475 
4.0
220340 
0.0
218124 
2.0
215313 
1.0
215076 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3270984
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row3.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
3.0 221475
18.5%
4.0 220340
18.4%
0.0 218124
18.2%
2.0 215313
17.9%
1.0 215076
17.9%
(Missing) 109672
9.1%

Length

2025-08-01T14:06:10.799920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-01T14:06:10.831014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.0 221475
20.3%
4.0 220340
20.2%
0.0 218124
20.0%
2.0 215313
19.7%
1.0 215076
19.7%

Most occurring characters

ValueCountFrequency (%)
0 1308452
40.0%
. 1090328
33.3%
3 221475
 
6.8%
4 220340
 
6.7%
2 215313
 
6.6%
1 215076
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3270984
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1308452
40.0%
. 1090328
33.3%
3 221475
 
6.8%
4 220340
 
6.7%
2 215313
 
6.6%
1 215076
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3270984
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1308452
40.0%
. 1090328
33.3%
3 221475
 
6.8%
4 220340
 
6.7%
2 215313
 
6.6%
1 215076
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3270984
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1308452
40.0%
. 1090328
33.3%
3 221475
 
6.8%
4 220340
 
6.7%
2 215313
 
6.6%
1 215076
 
6.6%

Education Level
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.2 MiB
Master's
303818 
PhD
303507 
Bachelor's
303234 
High School
289441 

Length

Max length11
Median length10
Mean length7.96438
Min length3

Characters and Unicode

Total characters9557256
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBachelor's
2nd rowMaster's
3rd rowHigh School
4th rowBachelor's
5th rowBachelor's

Common Values

ValueCountFrequency (%)
Master's 303818
25.3%
PhD 303507
25.3%
Bachelor's 303234
25.3%
High School 289441
24.1%

Length

2025-08-01T14:06:10.875155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-01T14:06:10.908631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
master's 303818
20.4%
phd 303507
20.4%
bachelor's 303234
20.4%
high 289441
19.4%
school 289441
19.4%

Most occurring characters

ValueCountFrequency (%)
h 1185623
12.4%
s 910870
 
9.5%
o 882116
 
9.2%
e 607052
 
6.4%
r 607052
 
6.4%
' 607052
 
6.4%
a 607052
 
6.4%
c 592675
 
6.2%
l 592675
 
6.2%
M 303818
 
3.2%
Other values (9) 2661271
27.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9557256
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
h 1185623
12.4%
s 910870
 
9.5%
o 882116
 
9.2%
e 607052
 
6.4%
r 607052
 
6.4%
' 607052
 
6.4%
a 607052
 
6.4%
c 592675
 
6.2%
l 592675
 
6.2%
M 303818
 
3.2%
Other values (9) 2661271
27.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9557256
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
h 1185623
12.4%
s 910870
 
9.5%
o 882116
 
9.2%
e 607052
 
6.4%
r 607052
 
6.4%
' 607052
 
6.4%
a 607052
 
6.4%
c 592675
 
6.2%
l 592675
 
6.2%
M 303818
 
3.2%
Other values (9) 2661271
27.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9557256
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
h 1185623
12.4%
s 910870
 
9.5%
o 882116
 
9.2%
e 607052
 
6.4%
r 607052
 
6.4%
' 607052
 
6.4%
a 607052
 
6.4%
c 592675
 
6.2%
l 592675
 
6.2%
M 303818
 
3.2%
Other values (9) 2661271
27.8%

Occupation
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing358075
Missing (%)29.8%
Memory size9.2 MiB
Employed
282750 
Self-Employed
282645 
Unemployed
276530 

Length

Max length13
Median length10
Mean length10.335463
Min length8

Characters and Unicode

Total characters8701685
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSelf-Employed
2nd rowSelf-Employed
3rd rowSelf-Employed
4th rowEmployed
5th rowEmployed

Common Values

ValueCountFrequency (%)
Employed 282750
23.6%
Self-Employed 282645
23.6%
Unemployed 276530
23.0%
(Missing) 358075
29.8%

Length

2025-08-01T14:06:10.951391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-01T14:06:10.981877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
employed 282750
33.6%
self-employed 282645
33.6%
unemployed 276530
32.8%

Most occurring characters

ValueCountFrequency (%)
e 1401100
16.1%
l 1124570
12.9%
m 841925
9.7%
p 841925
9.7%
o 841925
9.7%
y 841925
9.7%
d 841925
9.7%
E 565395
6.5%
S 282645
 
3.2%
f 282645
 
3.2%
Other values (3) 835705
9.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8701685
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1401100
16.1%
l 1124570
12.9%
m 841925
9.7%
p 841925
9.7%
o 841925
9.7%
y 841925
9.7%
d 841925
9.7%
E 565395
6.5%
S 282645
 
3.2%
f 282645
 
3.2%
Other values (3) 835705
9.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8701685
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1401100
16.1%
l 1124570
12.9%
m 841925
9.7%
p 841925
9.7%
o 841925
9.7%
y 841925
9.7%
d 841925
9.7%
E 565395
6.5%
S 282645
 
3.2%
f 282645
 
3.2%
Other values (3) 835705
9.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8701685
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1401100
16.1%
l 1124570
12.9%
m 841925
9.7%
p 841925
9.7%
o 841925
9.7%
y 841925
9.7%
d 841925
9.7%
E 565395
6.5%
S 282645
 
3.2%
f 282645
 
3.2%
Other values (3) 835705
9.6%

Health Score
Real number (ℝ)

Missing 

Distinct532655
Distinct (%)47.3%
Missing74076
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean25.613908
Minimum2.0122372
Maximum58.975914
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 MiB
2025-08-01T14:06:11.024741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.0122372
5-th percentile7.2858011
Q115.918959
median24.578648
Q334.527209
95-th percentile47.613406
Maximum58.975914
Range56.963677
Interquartile range (IQR)18.608251

Descriptive statistics

Standard deviation12.203462
Coefficient of variation (CV)0.4764389
Kurtosis-0.78499969
Mean25.613908
Median Absolute Deviation (MAD)9.2376174
Skewness0.28218731
Sum28839313
Variance148.92448
MonotonicityNot monotonic
2025-08-01T14:06:11.077494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.92724142 128
 
< 0.1%
25.90765016 124
 
< 0.1%
19.8697009 119
 
< 0.1%
22.95540237 104
 
< 0.1%
27.8450064 103
 
< 0.1%
27.9294023 101
 
< 0.1%
20.9777037 95
 
< 0.1%
20.63784183 94
 
< 0.1%
10.9581528 93
 
< 0.1%
23.95570971 91
 
< 0.1%
Other values (532645) 1124872
93.7%
(Missing) 74076
 
6.2%
ValueCountFrequency (%)
2.012237182 1
< 0.1%
2.024415229 1
< 0.1%
2.039338266 1
< 0.1%
2.053457869 1
< 0.1%
2.056558808 1
< 0.1%
2.060175622 1
< 0.1%
2.060433713 1
< 0.1%
2.064241318 1
< 0.1%
2.065447149 1
< 0.1%
2.068843225 1
< 0.1%
ValueCountFrequency (%)
58.97591405 1
< 0.1%
58.88603451 1
< 0.1%
58.5696892 1
< 0.1%
58.4524782 1
< 0.1%
58.40100949 1
< 0.1%
57.98884782 1
< 0.1%
57.92381001 1
< 0.1%
57.90318089 1
< 0.1%
57.85252539 1
< 0.1%
57.82680301 1
< 0.1%

Location
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.2 MiB
Suburban
401542 
Rural
400947 
Urban
397511 

Length

Max length8
Median length5
Mean length6.003855
Min length5

Characters and Unicode

Total characters7204626
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowRural
3rd rowSuburban
4th rowRural
5th rowRural

Common Values

ValueCountFrequency (%)
Suburban 401542
33.5%
Rural 400947
33.4%
Urban 397511
33.1%

Length

2025-08-01T14:06:11.125761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-01T14:06:11.155830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
suburban 401542
33.5%
rural 400947
33.4%
urban 397511
33.1%

Most occurring characters

ValueCountFrequency (%)
u 1204031
16.7%
b 1200595
16.7%
r 1200000
16.7%
a 1200000
16.7%
n 799053
11.1%
S 401542
 
5.6%
R 400947
 
5.6%
l 400947
 
5.6%
U 397511
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7204626
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 1204031
16.7%
b 1200595
16.7%
r 1200000
16.7%
a 1200000
16.7%
n 799053
11.1%
S 401542
 
5.6%
R 400947
 
5.6%
l 400947
 
5.6%
U 397511
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7204626
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 1204031
16.7%
b 1200595
16.7%
r 1200000
16.7%
a 1200000
16.7%
n 799053
11.1%
S 401542
 
5.6%
R 400947
 
5.6%
l 400947
 
5.6%
U 397511
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7204626
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 1204031
16.7%
b 1200595
16.7%
r 1200000
16.7%
a 1200000
16.7%
n 799053
11.1%
S 401542
 
5.6%
R 400947
 
5.6%
l 400947
 
5.6%
U 397511
 
5.5%

Policy Type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.2 MiB
Premium
401846 
Comprehensive
399600 
Basic
398554 

Length

Max length13
Median length7
Mean length8.3337433
Min length5

Characters and Unicode

Total characters10000492
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPremium
2nd rowComprehensive
3rd rowPremium
4th rowBasic
5th rowPremium

Common Values

ValueCountFrequency (%)
Premium 401846
33.5%
Comprehensive 399600
33.3%
Basic 398554
33.2%

Length

2025-08-01T14:06:11.194773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-01T14:06:11.222859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
premium 401846
33.5%
comprehensive 399600
33.3%
basic 398554
33.2%

Most occurring characters

ValueCountFrequency (%)
e 1600646
16.0%
m 1203292
12.0%
i 1200000
12.0%
r 801446
 
8.0%
s 798154
 
8.0%
P 401846
 
4.0%
u 401846
 
4.0%
C 399600
 
4.0%
o 399600
 
4.0%
p 399600
 
4.0%
Other values (6) 2394462
23.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000492
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1600646
16.0%
m 1203292
12.0%
i 1200000
12.0%
r 801446
 
8.0%
s 798154
 
8.0%
P 401846
 
4.0%
u 401846
 
4.0%
C 399600
 
4.0%
o 399600
 
4.0%
p 399600
 
4.0%
Other values (6) 2394462
23.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000492
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1600646
16.0%
m 1203292
12.0%
i 1200000
12.0%
r 801446
 
8.0%
s 798154
 
8.0%
P 401846
 
4.0%
u 401846
 
4.0%
C 399600
 
4.0%
o 399600
 
4.0%
p 399600
 
4.0%
Other values (6) 2394462
23.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000492
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1600646
16.0%
m 1203292
12.0%
i 1200000
12.0%
r 801446
 
8.0%
s 798154
 
8.0%
P 401846
 
4.0%
u 401846
 
4.0%
C 399600
 
4.0%
o 399600
 
4.0%
p 399600
 
4.0%
Other values (6) 2394462
23.9%

Previous Claims
Real number (ℝ)

Missing  Zeros 

Distinct10
Distinct (%)< 0.1%
Missing364029
Missing (%)30.3%
Infinite0
Infinite (%)0.0%
Mean1.0026891
Minimum0
Maximum9
Zeros305433
Zeros (%)25.5%
Negative0
Negative (%)0.0%
Memory size9.2 MiB
2025-08-01T14:06:11.256787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.98284017
Coefficient of variation (CV)0.98020431
Kurtosis0.74009082
Mean1.0026891
Median Absolute Deviation (MAD)1
Skewness0.90532101
Sum838219
Variance0.9659748
MonotonicityNot monotonic
2025-08-01T14:06:11.289680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 305433
25.5%
1 300811
25.1%
2 167661
14.0%
3 49011
 
4.1%
4 10668
 
0.9%
5 2018
 
0.2%
6 302
 
< 0.1%
7 58
 
< 0.1%
8 8
 
< 0.1%
9 1
 
< 0.1%
(Missing) 364029
30.3%
ValueCountFrequency (%)
0 305433
25.5%
1 300811
25.1%
2 167661
14.0%
3 49011
 
4.1%
4 10668
 
0.9%
5 2018
 
0.2%
6 302
 
< 0.1%
7 58
 
< 0.1%
8 8
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 8
 
< 0.1%
7 58
 
< 0.1%
6 302
 
< 0.1%
5 2018
 
0.2%
4 10668
 
0.9%
3 49011
 
4.1%
2 167661
14.0%
1 300811
25.1%
0 305433
25.5%

Vehicle Age
Real number (ℝ)

Zeros 

Distinct20
Distinct (%)< 0.1%
Missing6
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean9.5698887
Minimum0
Maximum19
Zeros61615
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size9.2 MiB
2025-08-01T14:06:11.326969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median10
Q315
95-th percentile19
Maximum19
Range19
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.7761887
Coefficient of variation (CV)0.60357951
Kurtosis-1.2064477
Mean9.5698887
Median Absolute Deviation (MAD)5
Skewness-0.020408882
Sum11483809
Variance33.364356
MonotonicityNot monotonic
2025-08-01T14:06:11.366394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
17 62577
 
5.2%
0 61615
 
5.1%
11 61483
 
5.1%
18 61399
 
5.1%
10 61351
 
5.1%
15 60817
 
5.1%
14 60815
 
5.1%
12 60718
 
5.1%
19 60495
 
5.0%
16 60314
 
5.0%
Other values (10) 588410
49.0%
ValueCountFrequency (%)
0 61615
5.1%
1 57365
4.8%
2 59741
5.0%
3 59058
4.9%
4 58148
4.8%
5 59499
5.0%
6 58084
4.8%
7 59700
5.0%
8 58298
4.9%
9 59833
5.0%
ValueCountFrequency (%)
19 60495
5.0%
18 61399
5.1%
17 62577
5.2%
16 60314
5.0%
15 60817
5.1%
14 60815
5.1%
13 58684
4.9%
12 60718
5.1%
11 61483
5.1%
10 61351
5.1%

Credit Score
Real number (ℝ)

Missing 

Distinct550
Distinct (%)0.1%
Missing137882
Missing (%)11.5%
Infinite0
Infinite (%)0.0%
Mean592.92435
Minimum300
Maximum849
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 MiB
2025-08-01T14:06:11.415149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile341
Q1468
median595
Q3721
95-th percentile822
Maximum849
Range549
Interquartile range (IQR)253

Descriptive statistics

Standard deviation149.98195
Coefficient of variation (CV)0.25295292
Kurtosis-1.0901024
Mean592.92435
Median Absolute Deviation (MAD)127
Skewness-0.11357262
Sum6.2975562 × 108
Variance22494.584
MonotonicityNot monotonic
2025-08-01T14:06:11.509054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
434 4288
 
0.4%
431 4277
 
0.4%
757 4021
 
0.3%
713 3977
 
0.3%
437 3887
 
0.3%
584 3800
 
0.3%
613 3758
 
0.3%
447 3742
 
0.3%
658 3730
 
0.3%
607 3721
 
0.3%
Other values (540) 1022917
85.2%
(Missing) 137882
 
11.5%
ValueCountFrequency (%)
300 856
0.1%
301 1575
0.1%
302 1187
0.1%
303 1061
0.1%
304 841
0.1%
305 699
 
0.1%
306 875
0.1%
307 1298
0.1%
308 1811
0.2%
309 1332
0.1%
ValueCountFrequency (%)
849 1796
0.1%
848 2227
0.2%
847 2040
0.2%
846 1843
0.2%
845 1671
0.1%
844 1957
0.2%
843 2052
0.2%
842 1886
0.2%
841 1860
0.2%
840 760
 
0.1%

Insurance Duration
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.0182192
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 MiB
2025-08-01T14:06:11.550380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5943313
Coefficient of variation (CV)0.51698247
Kurtosis-1.2372517
Mean5.0182192
Median Absolute Deviation (MAD)2
Skewness-0.0087933023
Sum6021858
Variance6.7305551
MonotonicityNot monotonic
2025-08-01T14:06:11.587929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
9 137781
11.5%
1 135072
11.3%
8 133800
11.2%
7 133592
11.1%
5 132253
11.0%
4 132182
11.0%
6 132141
11.0%
3 132018
11.0%
2 131160
10.9%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
1 135072
11.3%
2 131160
10.9%
3 132018
11.0%
4 132182
11.0%
5 132253
11.0%
6 132141
11.0%
7 133592
11.1%
8 133800
11.2%
9 137781
11.5%
ValueCountFrequency (%)
9 137781
11.5%
8 133800
11.2%
7 133592
11.1%
6 132141
11.0%
5 132253
11.0%
4 132182
11.0%
3 132018
11.0%
2 131160
10.9%
1 135072
11.3%
Distinct167381
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Memory size9.2 MiB
Minimum2019-08-17 15:21:39.080371
Maximum2024-08-15 15:21:39.287115
Invalid dates0
Invalid dates (%)0.0%
2025-08-01T14:06:11.638110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:11.706103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Customer Feedback
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing77824
Missing (%)6.5%
Memory size9.2 MiB
Average
377905 
Poor
375518 
Good
368753 

Length

Max length7
Median length4
Mean length5.0102827
Min length4

Characters and Unicode

Total characters5622419
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPoor
2nd rowAverage
3rd rowGood
4th rowPoor
5th rowPoor

Common Values

ValueCountFrequency (%)
Average 377905
31.5%
Poor 375518
31.3%
Good 368753
30.7%
(Missing) 77824
 
6.5%

Length

2025-08-01T14:06:11.967555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-01T14:06:11.995884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
average 377905
33.7%
poor 375518
33.5%
good 368753
32.9%

Most occurring characters

ValueCountFrequency (%)
o 1488542
26.5%
e 755810
13.4%
r 753423
13.4%
A 377905
 
6.7%
v 377905
 
6.7%
a 377905
 
6.7%
g 377905
 
6.7%
P 375518
 
6.7%
G 368753
 
6.6%
d 368753
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5622419
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1488542
26.5%
e 755810
13.4%
r 753423
13.4%
A 377905
 
6.7%
v 377905
 
6.7%
a 377905
 
6.7%
g 377905
 
6.7%
P 375518
 
6.7%
G 368753
 
6.6%
d 368753
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5622419
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1488542
26.5%
e 755810
13.4%
r 753423
13.4%
A 377905
 
6.7%
v 377905
 
6.7%
a 377905
 
6.7%
g 377905
 
6.7%
P 375518
 
6.7%
G 368753
 
6.6%
d 368753
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5622419
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1488542
26.5%
e 755810
13.4%
r 753423
13.4%
A 377905
 
6.7%
v 377905
 
6.7%
a 377905
 
6.7%
g 377905
 
6.7%
P 375518
 
6.7%
G 368753
 
6.6%
d 368753
 
6.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
True
601873 
False
598127 
ValueCountFrequency (%)
True 601873
50.2%
False 598127
49.8%
2025-08-01T14:06:12.016548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.2 MiB
Weekly
306179 
Monthly
299830 
Rarely
299420 
Daily
294571 

Length

Max length7
Median length6
Mean length6.0043825
Min length5

Characters and Unicode

Total characters7205259
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWeekly
2nd rowMonthly
3rd rowWeekly
4th rowDaily
5th rowWeekly

Common Values

ValueCountFrequency (%)
Weekly 306179
25.5%
Monthly 299830
25.0%
Rarely 299420
25.0%
Daily 294571
24.5%

Length

2025-08-01T14:06:12.052477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-01T14:06:12.093207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
weekly 306179
25.5%
monthly 299830
25.0%
rarely 299420
25.0%
daily 294571
24.5%

Most occurring characters

ValueCountFrequency (%)
l 1200000
16.7%
y 1200000
16.7%
e 911778
12.7%
a 593991
 
8.2%
W 306179
 
4.2%
k 306179
 
4.2%
M 299830
 
4.2%
o 299830
 
4.2%
n 299830
 
4.2%
t 299830
 
4.2%
Other values (5) 1487812
20.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7205259
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 1200000
16.7%
y 1200000
16.7%
e 911778
12.7%
a 593991
 
8.2%
W 306179
 
4.2%
k 306179
 
4.2%
M 299830
 
4.2%
o 299830
 
4.2%
n 299830
 
4.2%
t 299830
 
4.2%
Other values (5) 1487812
20.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7205259
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 1200000
16.7%
y 1200000
16.7%
e 911778
12.7%
a 593991
 
8.2%
W 306179
 
4.2%
k 306179
 
4.2%
M 299830
 
4.2%
o 299830
 
4.2%
n 299830
 
4.2%
t 299830
 
4.2%
Other values (5) 1487812
20.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7205259
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 1200000
16.7%
y 1200000
16.7%
e 911778
12.7%
a 593991
 
8.2%
W 306179
 
4.2%
k 306179
 
4.2%
M 299830
 
4.2%
o 299830
 
4.2%
n 299830
 
4.2%
t 299830
 
4.2%
Other values (5) 1487812
20.6%

Property Type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.2 MiB
House
400349 
Apartment
399978 
Condo
399673 

Length

Max length9
Median length5
Mean length6.33326
Min length5

Characters and Unicode

Total characters7599912
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHouse
2nd rowHouse
3rd rowHouse
4th rowApartment
5th rowHouse

Common Values

ValueCountFrequency (%)
House 400349
33.4%
Apartment 399978
33.3%
Condo 399673
33.3%

Length

2025-08-01T14:06:12.138589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-01T14:06:12.164994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
house 400349
33.4%
apartment 399978
33.3%
condo 399673
33.3%

Most occurring characters

ValueCountFrequency (%)
o 1199695
15.8%
e 800327
10.5%
t 799956
10.5%
n 799651
10.5%
H 400349
 
5.3%
u 400349
 
5.3%
s 400349
 
5.3%
A 399978
 
5.3%
p 399978
 
5.3%
a 399978
 
5.3%
Other values (4) 1599302
21.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7599912
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1199695
15.8%
e 800327
10.5%
t 799956
10.5%
n 799651
10.5%
H 400349
 
5.3%
u 400349
 
5.3%
s 400349
 
5.3%
A 399978
 
5.3%
p 399978
 
5.3%
a 399978
 
5.3%
Other values (4) 1599302
21.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7599912
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1199695
15.8%
e 800327
10.5%
t 799956
10.5%
n 799651
10.5%
H 400349
 
5.3%
u 400349
 
5.3%
s 400349
 
5.3%
A 399978
 
5.3%
p 399978
 
5.3%
a 399978
 
5.3%
Other values (4) 1599302
21.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7599912
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1199695
15.8%
e 800327
10.5%
t 799956
10.5%
n 799651
10.5%
H 400349
 
5.3%
u 400349
 
5.3%
s 400349
 
5.3%
A 399978
 
5.3%
p 399978
 
5.3%
a 399978
 
5.3%
Other values (4) 1599302
21.0%

Premium Amount
Real number (ℝ)

Distinct4794
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1102.5448
Minimum20
Maximum4999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 MiB
2025-08-01T14:06:12.204122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile49
Q1514
median872
Q31509
95-th percentile2869
Maximum4999
Range4979
Interquartile range (IQR)995

Descriptive statistics

Standard deviation864.99886
Coefficient of variation (CV)0.78454757
Kurtosis1.5185856
Mean1102.5448
Median Absolute Deviation (MAD)449
Skewness1.2409155
Sum1.3230538 × 109
Variance748223.03
MonotonicityNot monotonic
2025-08-01T14:06:12.255250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 4268
 
0.4%
24 3901
 
0.3%
20 3849
 
0.3%
23 3524
 
0.3%
28 3418
 
0.3%
26 3375
 
0.3%
48 3307
 
0.3%
29 3139
 
0.3%
100 3125
 
0.3%
27 3074
 
0.3%
Other values (4784) 1165020
97.1%
ValueCountFrequency (%)
20 3849
0.3%
21 362
 
< 0.1%
22 1698
 
0.1%
23 3524
0.3%
24 3901
0.3%
25 4268
0.4%
26 3375
0.3%
27 3074
0.3%
28 3418
0.3%
29 3139
0.3%
ValueCountFrequency (%)
4999 1
 
< 0.1%
4997 2
 
< 0.1%
4996 1
 
< 0.1%
4994 1
 
< 0.1%
4992 1
 
< 0.1%
4991 1
 
< 0.1%
4988 18
< 0.1%
4987 5
 
< 0.1%
4986 3
 
< 0.1%
4985 2
 
< 0.1%

Interactions

2025-08-01T14:06:04.304319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:05:57.276082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:05:58.161855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:05:59.099621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:00.013441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:00.882520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:01.648685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:02.568016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:03.492825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:04.401785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:05:57.380707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:05:58.260559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:05:59.200332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:00.114110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:00.964393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:01.751229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:02.668443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:03.580727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:04.497747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:05:57.476201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:05:58.351932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:05:59.297133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:00.206477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:01.041176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:01.855412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:02.833733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:03.672803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:04.611360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:05:57.576207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:05:58.450931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:05:59.396124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:00.305915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:01.119809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:01.962192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:02.927697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:03.761020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:04.701440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:05:57.662202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:05:58.535941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:05:59.483381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:00.388876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:01.206949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:02.051033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:03.013998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:03.843819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:04.804623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:05:57.773969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:05:58.633292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:05:59.591543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:00.490766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:01.284303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:02.152626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:03.113702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:03.932630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:04.900777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:05:57.869821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:05:58.729900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:05:59.695170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:00.597628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:01.357723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:02.251600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:03.207200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:04.019557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:05.005832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:05:57.967755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:05:58.827819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:05:59.796051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:00.697367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:01.447938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:02.356329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:03.298642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:04.108013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:05.119557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:05:58.057206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:05:59.000235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:05:59.900750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:00.797726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:01.532630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:02.463745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:03.392188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-01T14:06:04.200327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-01T14:06:12.301339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeAnnual IncomeCredit ScoreCustomer FeedbackEducation LevelExercise FrequencyGenderHealth ScoreInsurance DurationLocationMarital StatusNumber of DependentsOccupationPolicy TypePremium AmountPrevious ClaimsProperty TypeSmoking StatusVehicle Ageid
Age1.0000.0000.0030.0010.0010.0010.0000.001-0.0000.0010.0020.0010.0000.000-0.0020.0020.0020.001-0.002-0.000
Annual Income0.0001.000-0.1500.0000.0020.0030.0000.0130.0000.0000.0010.0020.0020.002-0.0610.0010.0010.001-0.000-0.000
Credit Score0.003-0.1501.0000.0000.0020.0000.0010.0100.0010.0030.0030.0040.0020.001-0.0440.0380.0010.0020.0010.001
Customer Feedback0.0010.0000.0001.0000.0000.0030.0010.0040.0000.0020.0020.0010.0020.0000.0010.0040.0020.0000.0000.000
Education Level0.0010.0020.0020.0001.0000.0020.0000.0040.0020.0020.0010.0010.0010.0020.0020.0020.0040.0010.0000.001
Exercise Frequency0.0010.0030.0000.0030.0021.0000.0000.0040.0010.0000.0000.0020.0010.0030.0010.0000.0010.0010.0010.000
Gender0.0000.0000.0010.0010.0000.0001.0000.0060.0000.0010.0020.0020.0010.0010.0020.0000.0010.0030.0010.000
Health Score0.0010.0130.0100.0040.0040.0040.0061.0000.0030.0040.0040.0050.0050.0030.0160.0020.0000.003-0.0000.001
Insurance Duration-0.0000.0000.0010.0000.0020.0010.0000.0031.0000.0020.0020.0020.0020.000-0.0000.0030.0020.0000.003-0.000
Location0.0010.0000.0030.0020.0020.0000.0010.0040.0021.0000.0010.0020.0000.0010.0020.0000.0000.0010.0010.003
Marital Status0.0020.0010.0030.0020.0010.0000.0020.0040.0020.0011.0000.0000.0040.0000.0000.0010.0020.0000.0010.001
Number of Dependents0.0010.0020.0040.0010.0010.0020.0020.0050.0020.0020.0001.0000.0000.0000.0040.0050.0030.0000.0030.001
Occupation0.0000.0020.0020.0020.0010.0010.0010.0050.0020.0000.0040.0001.0000.0000.0040.0030.0040.0010.0000.000
Policy Type0.0000.0020.0010.0000.0020.0030.0010.0030.0000.0010.0000.0000.0001.0000.0000.0020.0010.0020.0000.001
Premium Amount-0.002-0.061-0.0440.0010.0020.0010.0020.016-0.0000.0020.0000.0040.0040.0001.0000.0450.0020.0030.0010.001
Previous Claims0.0020.0010.0380.0040.0020.0000.0000.0020.0030.0000.0010.0050.0030.0020.0451.0000.0030.002-0.001-0.000
Property Type0.0020.0010.0010.0020.0040.0010.0010.0000.0020.0000.0020.0030.0040.0010.0020.0031.0000.0010.0030.000
Smoking Status0.0010.0010.0020.0000.0010.0010.0030.0030.0000.0010.0000.0000.0010.0020.0030.0020.0011.0000.0030.000
Vehicle Age-0.002-0.0000.0010.0000.0000.0010.001-0.0000.0030.0010.0010.0030.0000.0000.001-0.0010.0030.0031.000-0.001
id-0.000-0.0000.0010.0000.0010.0000.0000.001-0.0000.0030.0010.0010.0000.0010.001-0.0000.0000.000-0.0011.000

Missing values

2025-08-01T14:06:05.522160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-01T14:06:06.556089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-08-01T14:06:09.279587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idAgeGenderAnnual IncomeMarital StatusNumber of DependentsEducation LevelOccupationHealth ScoreLocationPolicy TypePrevious ClaimsVehicle AgeCredit ScoreInsurance DurationPolicy Start DateCustomer FeedbackSmoking StatusExercise FrequencyProperty TypePremium Amount
0019.0Female10049.0Married1.0Bachelor'sSelf-Employed22.598761UrbanPremium2.017.0372.05.02023-12-23 15:21:39.134960PoorNoWeeklyHouse2869.0
1139.0Female31678.0Divorced3.0Master'sNaN15.569731RuralComprehensive1.012.0694.02.02023-06-12 15:21:39.111551AverageYesMonthlyHouse1483.0
2223.0Male25602.0Divorced3.0High SchoolSelf-Employed47.177549SuburbanPremium1.014.0NaN3.02023-09-30 15:21:39.221386GoodYesWeeklyHouse567.0
3321.0Male141855.0Married2.0Bachelor'sNaN10.938144RuralBasic1.00.0367.01.02024-06-12 15:21:39.226954PoorYesDailyApartment765.0
4421.0Male39651.0Single1.0Bachelor'sSelf-Employed20.376094RuralPremium0.08.0598.04.02021-12-01 15:21:39.252145PoorYesWeeklyHouse2022.0
5529.0Male45963.0Married1.0Bachelor'sNaN33.053198UrbanPremium2.04.0614.05.02022-05-20 15:21:39.207847AverageNoWeeklyHouse3202.0
6641.0Male40336.0Married0.0PhDNaNNaNRuralBasic2.08.0807.06.02020-02-21 15:21:39.219432PoorNoWeeklyHouse439.0
7748.0Female127237.0Divorced2.0High SchoolEmployed5.769783SuburbanComprehensive1.011.0398.05.02022-08-08 15:21:39.181605AverageNoRarelyCondo111.0
8821.0Male1733.0Divorced3.0Bachelor'sNaN17.869551UrbanPremium1.010.0685.08.02020-12-14 15:21:39.198406AverageNoMonthlyCondo213.0
9944.0Male52447.0Married2.0Master'sEmployed20.473718UrbanComprehensive1.09.0635.03.02020-08-02 15:21:39.144722PoorNoDailyCondo64.0
idAgeGenderAnnual IncomeMarital StatusNumber of DependentsEducation LevelOccupationHealth ScoreLocationPolicy TypePrevious ClaimsVehicle AgeCredit ScoreInsurance DurationPolicy Start DateCustomer FeedbackSmoking StatusExercise FrequencyProperty TypePremium Amount
1199990119999055.0Female72384.0Single0.0High SchoolUnemployed13.661678UrbanBasic1.03.0789.05.02020-01-10 15:21:39.155231AverageYesMonthlyApartment231.0
1199991119999159.0Female23706.0Divorced4.0High SchoolSelf-Employed24.913204SuburbanComprehensiveNaN17.0NaN1.02021-06-22 15:21:39.188220GoodYesMonthlyApartment3381.0
1199992119999253.0Female6837.0Married2.0High SchoolSelf-Employed17.844235UrbanComprehensiveNaN15.0406.04.02021-01-09 15:21:39.281787GoodNoRarelyHouse1251.0
1199993119999338.0Male1607.0Married1.0High SchoolNaN18.552314SuburbanComprehensive0.012.0469.02.02022-08-10 15:21:39.132191GoodNoRarelyHouse1027.0
1199994119999434.0Male23456.0Single4.0Master'sSelf-Employed14.783439RuralBasicNaN12.0548.09.02023-06-09 15:21:39.134960GoodNoMonthlyApartment1584.0
1199995119999536.0Female27316.0Married0.0Master'sUnemployed13.772907UrbanPremiumNaN5.0372.03.02023-05-03 15:21:39.257696PoorNoDailyApartment1303.0
1199996119999654.0Male35786.0DivorcedNaNMaster'sSelf-Employed11.483482RuralComprehensiveNaN10.0597.04.02022-09-10 15:21:39.134960PoorNoWeeklyApartment821.0
1199997119999719.0Male51884.0Divorced0.0Master'sNaN14.724469SuburbanBasic0.019.0NaN6.02021-05-25 15:21:39.106582GoodNoMonthlyCondo371.0
1199998119999855.0MaleNaNSingle1.0PhDNaN18.547381SuburbanPremium1.07.0407.04.02021-09-19 15:21:39.190215PoorNoDailyApartment596.0
1199999119999921.0FemaleNaNDivorced0.0PhDNaN10.125323RuralPremium0.018.0502.06.02020-08-26 15:21:39.155231GoodYesMonthlyHouse2480.0